Readmissions occur commonly among the elderly, are associated with suboptimal care and worse clinical outcomes, and contribute substantially to healthcare costs. In September 2012, the Centers for Medicare and Medicaid Services implemented the Hospital Readmissions Reduction Program. The policy financially penalizes hospitals with higher-than-expected 30-day risk-adjusted readmission rates for adults age 65 and older with heart failure, myocardial infarction, and pneumonia, and, as of 2015, chronic obstructive pulmonary disease and arthroplasty of the hip and knee. Coronary artery bypass grafting will be added in 2017. Readmission rates have declined since 2012 but favorable effects on long-term outcomes and costs are not assured. Survival could improve if hospitals improve inpatient and transition-related care, although length of stay and use of formal post-acute care could rise as hospitals strive to ensure clinical stability after discharge. Conversely, hospitals could engage in gaming, such as readmitting only the sickest patients, substituting observation stays for readmissions, or postponing readmissions beyond 30 days. Barriers to necessary readmission could lead to greater utilization of emergency department visits and worse survival. As hospitals create new systems of care, both favorable and unfavorable effects could affect patients not targeted by the policy. This project seeks to evaluate the Program's effects on survival, healthcare utilization, and payments by Medicare, and to consider potential inadvertent effects. First, the project will evaluate how the policy has influenced beneficiaries' survival during the six months after hospitalization, an outcome important to patients. Second, the project will evaluate 30-day readmission rates, utilization of hospital and post-acute care, and payments by Medicare. Finally, the analysis will examine gaming by hospitals and spillover effects for populations not targeted by the policy. Methods will involve using Medicare administrative data to measure changes in clinical and economic outcomes from before to after policy implementation, using an interrupted time-series design. To present results in formats that are easy for policymakers and stakeholders to understand, the analysis will use estimates from the models in simulations that predict outcomes with and without the HRRP. Lastly, because hospitals may respond differently based on the percent of revenue at risk, we will examine whether effects differ across hospitals according to each hospital's share of patients with Medicare insurance or each hospital's penalty size.